3) Classification and Discriminant Channel Selection. We use
channel-wise spatial responses to train an XGBoost classifier to select
discriminant channnels. The channel-wise classification performance
curves measured by the area-under-the-curve (AUC) and the accuracy
(ACC) for the apple vs. orange subset in the CycleGAN [1]} and
ProGAN [2]} datasets are shown in Fig. REF . We observe a consistent
trend between the validation AUC and training AUC curves and can select a
couple of discriminant channels based on the peaks of the validation AUC
curves. In the experiments, we select two and three optimal filters from
each PixelHop unit for CycleGAN and ProGAN, respectively. As a result,
we have six and nine discriminant filters for CycleGAN and ProGAN, respectively, and use their soft decision
scores from the XGBoost classifier as features for the image-level
decision ensemble.
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